{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<a href=\"https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv\" target=\"_blank\"><img align=\"left\" src=\"data/cover.jpg\" style=\"width: 76px; height: 100px; background: white; padding: 1px; border: 1px solid black; margin-right:10px;\"></a>\n",
    "*This notebook contains an excerpt from the book [Machine Learning for OpenCV](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv) by Michael Beyeler.\n",
    "The code is released under the [MIT license](https://opensource.org/licenses/MIT),\n",
    "and is available on [GitHub](https://github.com/mbeyeler/opencv-machine-learning).*\n",
    "\n",
    "*Note that this excerpt contains only the raw code - the book is rich with additional explanations and illustrations.\n",
    "If you find this content useful, please consider supporting the work by\n",
    "[buying the book](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv)!*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Implementing Our First Support Vector Machine](06.01-Implementing-Your-First-Support-Vector-Machine.ipynb) | [Contents](../README.md) | [Detecting Pedestrians with Support Vector Machines](06.03-Additional-SVM-Exercises.ipynb) >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Detecting Pedestrians in the Wild\n",
    "\n",
    "We briefly talked about the difference between detection and recognition. While recognition\n",
    "is concerned with classifying objects (for example, as pedestrians, cars, bicycles, and so on),\n",
    "detection is basically answering the question: is there a pedestrian present in this image?\n",
    "\n",
    "The basic idea behind most detection algorithms is to split up an image into many small\n",
    "patches, and then classify each image patch as either containing a pedestrian or not. This is\n",
    "exactly what we are going to do in this section. In order to arrive at our own pedestrian\n",
    "detection algorithm, we need to perform the following steps:\n",
    "1. Build a database of images containing pedestrians. These will be our positive data samples.\n",
    "2. Build a database of images not containing pedestrians. These will be our negative data samples.\n",
    "3. Train an SVM on the dataset.\n",
    "4. Apply the SVM to every possible patch of a test image in order to decide whether the overall image contains a pedestrian."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Obtaining the dataset\n",
    "\n",
    "For the purpose of this section, we will work with the MIT People dataset, which we are\n",
    "free to use for non-commercial purposes. So make sure not to use this in your\n",
    "groundbreaking autonomous start-up company before obtaining a corresponding software\n",
    "license.\n",
    "\n",
    "The dataset can be obtained from http://cbcl.mit.edu/software-datasets/PedestrianData.html. There you should find a DOWNLOAD button that leads you to a file called http://cbcl.mit.edu/projects/cbcl/software-datasets/pedestrians128x64.tar.gz.\n",
    "\n",
    "However, if you followed our installation instructions from earlier and\n",
    "checked out the code on GitHub, you already have the dataset and are\n",
    "ready to go! The file can be found at\n",
    "`notebooks/data/pedestrians128x64.tar.gz`. Since we are supposed to run this code from a Jupyter Notebook in the `notebooks/`\n",
    "directory, the relative path to the data directory is simply `data/`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "datadir = \"data/chapter6\"\n",
    "dataset = \"pedestrians128x64\"\n",
    "datafile = \"%s/%s.tar.gz\" % (datadir, dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The first thing to do is to unzip the file. We will extract all files into their own subdirectories\n",
    "in `data/pedestrians128x64/`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "extractdir = \"%s/%s\" % (datadir, dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We can do this either by hand (outside of Python) or with the following function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def extract_tar(datafile, extractdir):\n",
    "    try:\n",
    "        import tarfile\n",
    "    except ImportError:\n",
    "        raise ImportError(\"You do not have tarfile installed. \"\n",
    "                          \"Try unzipping the file outside of Python.\")\n",
    "\n",
    "    tar = tarfile.open(datafile)\n",
    "    tar.extractall(path=extractdir)\n",
    "    tar.close()\n",
    "    print(\"%s successfully extracted to %s\" % (datafile, extractdir))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Then we can call the function like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/chapter6/pedestrians128x64.tar.gz successfully extracted to data/chapter6\n"
     ]
    }
   ],
   "source": [
    "extract_tar(datafile, datadir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The dataset comes with a total of 924 color images of pedestrians, each scaled to 64 x 128\n",
    "pixels and aligned so that the person's body is in the center of the image. Scaling and\n",
    "aligning all data samples is an important step of the process, and we are glad that we don't\n",
    "have to do it ourselves.\n",
    "\n",
    "These images were taken in Boston and Cambridge in a variety of seasons and under\n",
    "several different lighting conditions. We can visualize a few example images by reading the\n",
    "image with OpenCV and passing an RGB version of the image to Matplotlib:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
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KYt9BZ8wnvcJGFUmgN2Te0LubZJRk3x44D4izb7nkp6bJuDtz/Gm6QekLwYDJ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g7LwD1eO/o0kZRg313DijMJ2DrrKYwHCIXp8tTPI8ghbF24mZHP+u2BtytXjRLMaiprKKoEdz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K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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9871dbeeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "for i in range(5):\n",
    "    filename = \"%s/per0010%d.ppm\" % (extractdir, i)\n",
    "    img = cv2.imread(filename)\n",
    "\n",
    "    plt.subplot(1, 5, i + 1)\n",
    "    plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We can look at additional pictures, but it's pretty clear that there is no straightforward way\n",
    "to describe pictures of people as easily as we did in the previous section for + and - data\n",
    "points. Part of the problem is thus finding a good way to represent these images. Does this\n",
    "ring a bell? It should! We're talking about feature engineering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Taking a glimpse at the histogram of oriented gradients (HOG)\n",
    "\n",
    "The HOG might just provide the help we're looking for in order to get this project done.\n",
    "HOG is a feature descriptor for images, much like the ones we discussed in [Chapter 4](),\n",
    "*Representing Data and Engineering Features*. It has been successfully applied to many different\n",
    "tasks in computer vision, but seems to work especially well for classifying people.\n",
    "\n",
    "The essential idea behind HOG features is that the local shapes and appearance of objects\n",
    "within an image can be described by the distribution of edge directions. The image is\n",
    "divided into small connected regions, within which a histogram of gradient directions (or\n",
    "**edge directions**) is compiled. Then, the descriptor is assembled by concatenating the\n",
    "different histograms. Please refer to the book for an example illustration.\n",
    "\n",
    "The HOG descriptor is fairly accessible in OpenCV by means of `cv2.HOGDescriptor`,\n",
    "which takes a bunch of input arguments, such as the detection window size (minimum size\n",
    "of the object to be detected, 48 x 96), the block size (how large each box is, 16 x 16), the cell\n",
    "size (8 x 8), and the cell stride (how many pixels to move from one cell to the next, 8 x 8).\n",
    "For each of these cells, the HOG descriptor then calculates a histogram of oriented gradients\n",
    "using nine bins:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "win_size = (48, 96)\n",
    "block_size = (16, 16)\n",
    "block_stride = (8, 8)\n",
    "cell_size = (8, 8)\n",
    "num_bins = 9\n",
    "hog = cv2.HOGDescriptor(win_size, block_size, block_stride, cell_size, num_bins)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Although this function call looks fairly complicated, these are actually the only values for\n",
    "which the HOG descriptor is implemented. The argument that matters the most is the\n",
    "window size (`win_size`).\n",
    "\n",
    "All that's left to do is call hog.compute on our data samples. For this, we build a dataset of\n",
    "positive samples (`X_pos`) by randomly picking pedestrian images from our data directory.\n",
    "In the following code snippet, we randomly select 400 pictures from the over 900 available,\n",
    "and apply the HOG descriptor to them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Could not find image data/chapter6/pedestrians128x64/per00000.ppm\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "random.seed(42)\n",
    "X_pos = []\n",
    "for i in random.sample(range(900), 400):\n",
    "    filename = \"%s/per%05d.ppm\" % (extractdir, i)\n",
    "    img = cv2.imread(filename)\n",
    "    if img is None:\n",
    "        print('Could not find image %s' % filename)\n",
    "        continue\n",
    "    X_pos.append(hog.compute(img, (64, 64)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "We should also remember that OpenCV wants the feature matrix to contain 32-bit floating\n",
    "point numbers, and the target labels to be 32-bit integers. We don't mind, since converting\n",
    "to NumPy arrays will allow us to easily investigate the sizes of the matrices we created:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((399, 1980, 1), (399,))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_pos = np.array(X_pos, dtype=np.float32)\n",
    "y_pos = np.ones(X_pos.shape[0], dtype=np.int32)\n",
    "X_pos.shape, y_pos.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "It looks like we picked a total of 399 training samples, each of which have 1,980 feature\n",
    "values (which are the HOG feature values)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Generating negatives\n",
    "\n",
    "The real challenge, however, is to come up with the perfect example of a non-pedestrian.\n",
    "After all, it's easy to think of example images of pedestrians. But what is the opposite of a\n",
    "pedestrian?\n",
    "\n",
    "This is actually a common problem when trying to solve new machine learning problems.\n",
    "Both research labs and companies spend a lot of time creating and annotating new datasets\n",
    "that fit their specific purpose.\n",
    "\n",
    "If you're stumped, let me give you a hint on how to approach this. A good first\n",
    "approximation to finding the opposite of a pedestrian is to assemble a dataset of images that\n",
    "look like the images of the positive class but do not contain pedestrians. These images could\n",
    "contain anything like cars, bicycles, streets, houses, and maybe even forests, lakes, or\n",
    "mountains.\n",
    "\n",
    "A good place to start is the Urban and Natural Scene dataset by the Computational Visual\n",
    "Cognition Lab at MIT. The complete dataset can be obtained from http://cvcl.mit.edu/database.htm, but don't bother. I have already assembled a good amount of images from\n",
    "categories such as open country, inner cities, mountains, and forests. You can find them in\n",
    "the `data/pedestrians_neg` directory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/chapter6/pedestrians_neg.tar.gz successfully extracted to data/chapter6\n"
     ]
    }
   ],
   "source": [
    "negset = \"pedestrians_neg\"\n",
    "negfile = \"%s/%s.tar.gz\" % (datadir, negset)\n",
    "negdir = \"%s/%s\" % (datadir, negset)\n",
    "extract_tar(negfile, datadir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "All the images are in color, in `.jpeg` format, and are 256 x 256 pixels. However, in order to\n",
    "use them as samples from a negative class that go together with our images of pedestrians\n",
    "earlier, we need to make sure that all images have the same pixel size. Moreover, the things\n",
    "depicted in the images should roughly be at the same scale. Thus, we want to loop through\n",
    "all the images in the directory (via `os.listdir`) and cut out a 64 x 128 **region of interest\n",
    "(ROI)**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "hroi = 128\n",
    "wroi = 64\n",
    "X_neg = []\n",
    "for negfile in os.listdir(negdir):\n",
    "    filename = '%s/%s' % (negdir, negfile)\n",
    "    img = cv2.imread(filename)\n",
    "    img = cv2.resize(img, (512, 512))\n",
    "    for j in range(5):\n",
    "        rand_y = random.randint(0, img.shape[0] - hroi)\n",
    "        rand_x = random.randint(0, img.shape[1] - wroi)\n",
    "        roi = img[rand_y:rand_y + hroi, rand_x:rand_x + wroi, :]\n",
    "        X_neg.append(hog.compute(roi, (64, 64)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "What did we almost forget? Exactly, we forgot to make sure that all feature values are 32-bit\n",
    "floating point numbers. Also, the target label of these images should be -1, corresponding to\n",
    "the negative class:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((250, 1980, 1), (250,))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_neg = np.array(X_neg, dtype=np.float32)\n",
    "y_neg = -np.ones(X_neg.shape[0], dtype=np.int32)\n",
    "X_neg.shape, y_neg.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Then we can concatenate all positive (`X_pos`) and negative samples (`X_neg`) into a single\n",
    "dataset `X`, which we split using the all too familiar `train_test_split` function from scikitlearn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "X = np.concatenate((X_pos, X_neg))\n",
    "y = np.concatenate((y_pos, y_neg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn import model_selection as ms\n",
    "X_train, X_test, y_train, y_test = ms.train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Implementing the support vector machine\n",
    "\n",
    "We already know how to build an SVM in OpenCV, so there's nothing much to see here.\n",
    "Planning ahead, we wrap the training procedure into a function, so that it's easier to repeat\n",
    "the procedure in the future:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def train_svm(X_train, y_train):\n",
    "    svm = cv2.ml.SVM_create()\n",
    "    svm.train(X_train, cv2.ml.ROW_SAMPLE, y_train)\n",
    "    return svm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The same can be done for the scoring function. Here we pass a feature matrix X and a label\n",
    "vector y, but we do not specify whether we're talking about the training or the test set. In\n",
    "fact, from the viewpoint of the function, it doesn't matter what set the data samples belong\n",
    "to, as long as they have the right format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def score_svm(svm, X, y):\n",
    "    from sklearn import metrics\n",
    "    _, y_pred = svm.predict(X)\n",
    "    return metrics.accuracy_score(y, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Then we can train and score the SVM with two short function calls:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "svm = train_svm(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_svm(svm, X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.64615384615384619"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_svm(svm, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Thanks to the HOG feature descriptor, we make no mistake on the training set. However,\n",
    "our generalization performance is quite abysmal (64.6 percent), as it is much less than the\n",
    "training performance (100 percent). This is an indication that the model is **overfitting** the\n",
    "data. The fact that it is performing way better on the training set than the test set means that\n",
    "the model has resorted to memorizing the training samples, rather than trying to abstract it\n",
    "into a meaningful decision rule.\n",
    "\n",
    "What can we do to improve the model performance?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Bootstrapping the model\n",
    "\n",
    "An interesting way to improve the performance of our model is to use **bootstrapping**. This\n",
    "idea was actually applied in one of the first papers on using SVMs in combination with\n",
    "HOG features for pedestrian detection. So let's pay a little tribute to the pioneers and try to\n",
    "understand what they did.\n",
    "\n",
    "Their idea was quite simple. After training the SVM on the training set, they scored the\n",
    "model and found that the model produced some false positives. Remember that false\n",
    "positive means that the model predicted a positive (+) for a sample that was really a\n",
    "negative (-). In our context, this would mean the SVM falsely believed an image to contain a\n",
    "pedestrian. If this happens for a particular image in the dataset, this example is clearly\n",
    "troublesome. Hence, we should add it to the training set and retrain the SVM with the\n",
    "additional troublemaker, so that the algorithm can learn to classify that one correctly. This\n",
    "procedure can be repeated until the SVM gives satisfactory performance.\n",
    "\n",
    "We will talk about bootstrapping in more detail in [Chapter 11](11.00-Selecting-the-Right-Model-with-Hyper-Parameter-Tuning.ipynb), *Selecting the Right Model with Hyperparameter Tuning*.\n",
    "\n",
    "Let's do the same. We will repeat the training procedure a maximum of three times. After\n",
    "each iteration, we identify the false positives in the test set and add them to the training set\n",
    "for the next iteration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "score_train = []\n",
    "score_test = []\n",
    "for j in range(3):\n",
    "    svm = train_svm(X_train, y_train)\n",
    "    score_train.append(score_svm(svm, X_train, y_train))\n",
    "    score_test.append(score_svm(svm, X_test, y_test))\n",
    "    \n",
    "    _, y_pred = svm.predict(X_test)\n",
    "    false_pos = np.logical_and((y_test.ravel() == -1), (y_pred.ravel() == 1))\n",
    "    if not np.any(false_pos):\n",
    "        print('done')\n",
    "        break\n",
    "    X_train = np.concatenate((X_train, X_test[false_pos, :]), axis=0)\n",
    "    y_train = np.concatenate((y_train, y_test[false_pos]), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "This allows us to improve the model over time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0, 1.0]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.64615384615384619, 1.0]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Here, we achieved 64.6 percent accuracy in the first round, but were able to get that up to a\n",
    "perfect 100 percent in the second round."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Detecting pedestrians in a larger image\n",
    "\n",
    "What's left to do is to connect the SVM classification procedure with the process of\n",
    "detection. The way to do this is to repeat our classification for every possible patch in the\n",
    "image. This is similar to what we did earlier when we visualized decision boundaries; we\n",
    "created a fine grid and classified every point on that grid. The same idea applies here. We\n",
    "divide the image into patches and classify every patch as either containing a pedestrian or\n",
    "not.\n",
    "\n",
    "Therefore, if we want to do this, we have to loop over all possible patches in an image, each\n",
    "time shifting our region of interest by a small number of `stride` pixels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "img_test = cv2.imread('data/chapter6/pedestrian_test.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "stride = 16\n",
    "found = []\n",
    "for ystart in np.arange(0, img_test.shape[0], stride):\n",
    "    for xstart in np.arange(0, img_test.shape[1], stride):\n",
    "        if ystart + hroi > img_test.shape[0]:\n",
    "            continue\n",
    "        if xstart + wroi > img_test.shape[1]:\n",
    "            continue\n",
    "        roi = img_test[ystart:ystart + hroi, xstart:xstart + wroi, :]\n",
    "        feat = np.array([hog.compute(roi, (64, 64))])\n",
    "        _, ypred = svm.predict(feat)\n",
    "        if np.allclose(ypred, 1):\n",
    "            found.append((ystart, xstart, hroi, wroi))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Because pedestrians could appear not just at various locations but also in various sizes, we\n",
    "would have to rescale the image and repeat the whole process. Thankfully, OpenCV has a\n",
    "convenience function for this **multi-scale detection task** in the form of the\n",
    "`detectMultiScale` function. This is a bit of a hack, but we can pass all SVM parameters to\n",
    "the `hog` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "hog = cv2.HOGDescriptor(win_size, block_size, block_stride, cell_size, num_bins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "rho, _, _ = svm.getDecisionFunction(0)\n",
    "sv = svm.getSupportVectors()\n",
    "hog.setSVMDetector(np.append(sv[0, :].ravel(), rho))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "In practice, when people are faced with a standard task such as pedestrian detection, they\n",
    "often rely on precanned SVM classifiers that are built into OpenCV. This is the method that\n",
    "I hinted at in the very beginning of this chapter. By loading either\n",
    "`cv2.HOGDescriptor_getDaimlerPeopleDetector()` or\n",
    "`cv2.HOGDescriptor_getDefaultPeopleDetector()`, we can get started with only a\n",
    "few lines of code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "hogdef = cv2.HOGDescriptor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "hogdef.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "found, _ = hogdef.detectMultiScale(img_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Then we can mark the detected pedestrians in the image by looping over the bounding\n",
    "boxes in found:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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J1GrKIpfe45XW12aq6tAm7lxXphd8MT8ueTQqxhiXRGT1Zpzkk8hnQpGDCER/\ngUIoD5s0H24NRhuHifrJL5UCYanqItwlzTJUJ3B1cHALXZVUtXN7rNW8+eaXePLsiHv3XHnWZ8+e\nkfb6jMdj6oaS6DnqeemuM8aQpk6RNzHSu/depzaWQ8+9XiwWHBwcsL29G1zBmzdvusxSC1XuB9HI\n8VJDHQZjQiXHW7ecIhsOh2ATdO3OyQvDclkTRbItYztb+KCd6ShyV6Ol9ouG1trx40XU0sN8TYeG\nGiWEoK4MNpS+cpi8tZY07QEOJmrqszcLaRxJkiRG0SrG6fkpP/j+d0Nw6M/9xq8z2t6iWK+Y+YJf\nk50J26Mhk0E/9Pl0do60MPNc4KqqUHGKUjIkbDjeaJtFJyK1UfLzZeJc2E0e+cuU7Iv5C1dDLx83\nwRoq2mXKYVdJv6zM7At0QyEw9YtK9jKfWcqOkrri2y7z2KHBbTswkNyEX9q2aI0XEL7cbwPLeDoi\nbS0VrR0EGjcJcGVNvl4hraHw0Fld16RJwtGzZ2R+Pk/PL5henGE9/DK9OGM+vUDrPvt7LggvMRhT\nc+rHkhBQrHMG/T65NzDy2Yx+v0+SpBhfuVPXEmoZ6v0b7YbTYjojSV1/rZcrZF1tjKdmvNe+D6LI\n94FpNwuxtdngzTctfFVuwAvjxtoQSM7znEiATFQnMfGFrnypfCYUuRC0qbK+EJYUNhSrkUIg44go\nkmHlVVGElRLRlLA2hqousTZGSNdhd2/fJF/PUZFjg1iruXlrDxXB3r4rSHV+cUIUC4bDjDhzSlIo\nSVXXjK0LYpVVjq5dckTjJfQHGb/yK7/CZOxKzf7Tf/p/8Ku/+mvs7u5y84bDfu+9co/nR88YDoeh\nXGYcpQhp0cYN6sViQb/fZ5G3vPWmwFKTDSmlC75kWUbmAyGRVD7Y1gmQNQ3alPHVGkwMouVouzhD\nhPUV6LRw+J6K2vsYY4ikojBdnryl38/CAG244A5jbCy7hFWR8+7P3B7bs8WUu/fuMJpsh/LDwgw5\nOnzK3t4e85lnIHkPqVm446xHbdqsuyBmM13cjZ2XJMh0/o1tPbfGu7O8qOCuSqLp/vuqZKOudNki\nL+ODX3X/bmJNd9I7JX2JH+5x+e6x7oYYlz2LqyRY1I0yauOnP1c2YzIKG/qkVVx1ZZDW87EtbsOX\njidU5BX5Ys5gMEB6xdlLM86rOij7PF/RH/To9VN6vi5/VeQokVJ4AytNE4qiIkkipOwwh+qatTYI\nfDVNLREEZJmXAAAgAElEQVTErtCd/4bJ1ojpqqL27z2bX8BatfEO48t7iLZPiqJySWyizTZd5TlW\nEAwsIQRlVWzmOIQO6xgDRviGfzEnoO2/z5lFLhChprWpKqpSU0USvGIdDodYa8j6PWYzT7+T1mdb\nuo/NyzVFOcfEMVnsKUT5nOnpU77+ta8BoCUspmfcPthnsfBJBRGcnZ8wHg2JPOUNa6hMRZPVVmrB\ncj1HioSmM+6//z6vf+nLbjMH4Hd/9/c4Pz9lOBnSH7tjWS9BRoJMpiGQqqLGLfUsjnIJnkVQ+Sj7\ncpGDjSg8G6/WkuUiR5CGrama7FFZ1WQ+c1XJmEjYkC3nmBKuDHDw4LxCr0xTs0ZibU5MHOrQHBzc\n4YMPPuB8ekHlaYpVqQMVEaBYLznYHzO/aFkrvZ7i7iuvBUv7/OKE9/IZN2/e5OiJq7b4hTe+gLDw\n53/9z5P6d8lGAweVeEW+WK6Ikp6rKteluomW/KeMxdQGK2ygqUoItVnCtG6q8XWTM/yfXpb92YgL\nBHcCVmZT8YdrvStcdwJ/m55CAzV4JW38PUR775YmWYdrhGVjMwYXxHTeqfu0OrRLlqT+XuAUwKZq\ndp/f0O+g9gltAMIajBRoa8PGflY32dOKlqnr4M6WWmgwUmGFRfn2zSLFarGm8FUye3HEIE0QGIw3\nwg5nFxw+fIioS+YL54Ut8zMuFschaChExe27N+hlGau5C57X1n1vk20sFSyXS7epjJ8PSazQ2tV7\naVg1RZGDTTChSwT7+/vMygWJZz0tFgsQaqPiaV3XiEh1jABQaQymDVw38yn2EI0UyhmVZbGhyIVQ\nbZdYXC0ha2mUtRQSiUVGEVa3QftPKtfBzmu5lmu5ls+5fCYscoC44UUrha5cULMJIDQ/I7/SAlS1\nQYi6TUaRjlI1Ho+5te/qDud5zmqZozyfUyA5PH7CYDBiNvXbQFWG5WzFelXSH/sdPUpDWRmUd2tN\nDWVhUKpGeExrubjgxnRG4q34r33tl7m4mDI7vwgWqcMTI4piHVwvax0nvlnRkySjqpy123JKBYIU\n3ewooiIGfYjkIFQxbKRJ+OhK49ZHfmNnKaPA7TZVDbrF6IVw7q8wrVWaxQnFak1VlAES0VWFpbND\nk1Skccx5VQTPcTlfOz6sh4isqalLy3I+a93TesnRkyN++sO3mEwc9/fmwQESw7/+r/0bANzY2WOW\nF5SoQJts0rzxFrJL7tIbCK65DKngaV0WX9wLYJN293HiMOpOcP2SBFe4CZ4Hq3qTkuc2IZGhzbWv\ntdLQ/a7ijze3uCoGoBpLXnVhpU6xtiu8C8fkbr+rrus2ZV9529yYkH7u3mOzsJMQ4pKVKN3YQbbB\nTgSxIJRtsNr4sWfCGBTGEknJ8dkJT5985F+6Zj6fu0Ao0O8n7OzsUJUlhY+DlYWrjZ6lTX1wS5r1\nMMaQpS0tV8UKBQFKWa1L6qqkLJudjSQ3VYzRluHAXVdVFWWVb8Qaqqoi6hTp6vnaS5U2AeKL4xQh\nbCjfIaUlVhEVxQv1awKVVHbb7nKfWVT0IhTz8+SzocitDVhsGqfYNKE2VSjGVOQVVhhXzdB/W16V\n1DVhPz1jDVmWcOfgFm/+kmNNnJ8cY6saXftzhEXXgqcPD0Oiy/OjUxaLFdYqrPZlbHWNrkElzYCJ\nECQIVKijsrU15Pnz50z8NlR/4S/8Bb797W9TllUIeDV7jy6Xa3qZ44jX1WbNmPFwQlFUDAYZtWey\n1JUlUhnrlU/gME4ZRLEMGF6jYExtQ/Zfszdk6/obnyTVBrbqukZrHfizwiv7SKoQsFos5uR5zmAw\nYG/bxRdOTg+xxjDyA380GPOj773F3s5+cD0Pbu5TG8jXLtgbJTHGVhw9exTaqTeISGPBs6cPOX7u\n2un+ez9DGMsXfKJHXdcMtvaotA6wBcLFCmzDqvAZrYnouPpBC7WLW5NVaP2BOIo2skGbdru81Zvr\nI90pVdutWNe5FkvSOSfEN7qBSymc6yzb+4rOBuBSSrf4W4vQLXwjrMNlm3Oa/1TYulCGRaTdVUZs\nYO7hHZqgpGsUTK1pY5Z+sbOm9eeVRCqJrs3mPS4zYho933yvNmRxQuQx62pduHKyRrNaOhguiyPu\nHNzi6MlHrFYuTjLo9+hnCSuvtKNYIZVgOp+SeuNhsZwigNq3UZwodnZuYK0OsOh8eUgUKYf/+1d1\nG7WDtc3u9o6BkqZpmAdGt1uydfsyEjLMVSkls9mMum7nWJJE1KYmX+ah3/pZ7wrm0IsBd2FarL3K\nC7TVRHGbpPRzQh0b8tlQ5EJsFMOKY4Uk8inxhJ+qINCFstRhuhc+gr29M+LNN9/kpz/+CcumXnFZ\nIbQOdCWNZTqbMxiMmHtmydbONnu7PW7euMVs5Toj7WWMB1tBaZyeTLlx6667j6cObW2N+e53v8vt\n2+7ddrf3+Ot/7a/xR3/8bX7wA7cTdy8boGtLlmWBopamGdZazo4c2+WVO/dI44TD4+f0h05Jam2Z\nDIdhosdJxmqtOTs7C4p85P8eqaRNmTaGXpaFlb/f66GiiOV8EayJXi9FqXaiawzlauXqOvtBPBwO\nKeoKq2se+31DR8OMVb7ixNePfvbkkC9/+csU6zat3uISIZoyAvuTXZ4dPWM8HnFj31VyLKqCYZYx\n6fdRvvrho0cPUVLyT/7x7wFwcj7lxiuv8Zf/w7/KZNvvxbiac/vOHSpvVx4/P2Vra4uiKkMyWaJi\npIBilXP7lgs4X5ydoYQk9gv3bDZDRdEG/c7ikjy6WaKu6JENFlpTD797TEaKNE5aaqf1C6m5NGmJ\nMEYHnN491nSURkthVDTJXG5haQLgbocq69/bnbv2iVdJkgRPaT6dIYRL6mrkYjbFWhuOHR4+ZTwe\nhyxWrTXCgDWapkyx8O9emToomzTLODs/D9U1lVCs8zWj0Yii9LtUFTmpjMKWaZOdAfffPkJYy91X\n3EL9L//wD5henHP47Emg362Xc5bLOalnkdy4scfJyXN6vR4X524B2LtxC9sJIv7tv/N3mM8uGI3H\n/Gd/82+6b7FwfnpCXWkSH2PrZQOXXJS0WeBHx8/Z2t6h2Us2ThSPnrQY/WAwoN/vs1wu6Q0bptgs\n9GkTX8jzHIPeNN4iRT+Nw7Hlwu8X6j2EPM/p9XpIIam8bouzFKsLX3V1xC8q1xj5tVzLtVzL51w+\nGxY5rUdn8RF2KTbwQrerjggYtTWCsK1Qc613/bo1fmQn6u7wUulpfQ09TCBV5K2l9pg1IsAvjtlB\n+9wgJrhMDRvCud+XqGBW0vAorBQIQ6iR3tTcFqLldjc7FrXumUOCHXOiqXvhv0O1LpiwOIpeB8kM\naNulpBXTaTsj2v+gtTi68MDlAlIuscbDAOFWjthng6Xr6zBbkA2P3bTMk82yqpdx3UtQh3VcZWUb\nqqJwmK5ov9K93yUKoXf9g/Utmp+BNIKuTMCpP46CeJl2ePlchQALQsoACbUedTuGHBtKwCUMVEoZ\n4KGXPcO/sf9bA/eYzubUbbJK852XaZbC1/xvWBzCSgR+1xvZGbv28hu6P4djwo0RqWghMFMjpEQ1\n32p9qTNhQls4WMFtbtKMC6n8mBPtuxpjWoYPLXoTRPgIiZAI1aiyAotAdDY/ttZujG+E2NikJdzu\ncrCJzf5v7gV2o02laJ+lsUSXWFBXJWhd1a9tAYpfXD5DitxjvgZq4wZ6g5FL4baKiiKJ8u7KfL7g\n5q1d/uyX/hwA+/vbYLUvNO/c//PjczKhmAqXaWkFyCjj/GIZiuOIaMVke5uHDx9jfXOIOGIwGoaC\nXMPBhFQmrPI50tPBLi4ueOWVV5jPndu3u7vP0fNnvPr6F1g1vEHhsNfJ1ogHD+4DMB4MEFb4jSHg\n/Pycra0tdnZ2OJ85mEjKiMePnnDv7hsAPHr8jOFwwtHhGYORc2uX87zddadxy/1O7Q1H3GpDZSsi\n2fJj1+u1L5jfwgjCWmzdQlBCCAZZD0QUqsOVRQ7ShMDxeDxmtVq5Wta1D5JmGdpoBqMGxumxs7MT\nAr3NO8o0pd/vMxw4FzLrpYjOjkcyTh20YWAwGIbrirwKm06XZekmgzahyFA/zcBvT9a4yOsiZ9gf\nkPq+zOsqTPJmfkVxjBQCJdqqfd0Nv5s2aZRLUCz65YvdxgYWvl53O/nhBSVucf3WTcjR7SLZQD2u\nH41v7wRwSVHTuXP7VeTw+XXRQl7NItZAlHEcs1iswmYncRyTqBgt2yBrkzgmRFeRGkajAZI2iaWs\ncuZzED4ovTUckC+W4MfJhw/eZ71YIoTmW//oH7pzJmOSWIExlB7bj42g3+sR+0BmXZcYU7vaLT7r\ne2vXzZkm2/i//+/+R05OnjMej3n02G0Rl6YpkcqohMbXXKQyFl1XgUob2iReU/kYQGXsxjaS7ntt\nSKprfr+cUOaC8J14hB83VraF0wLP38/XkDHbWaSqqnLBT9WtiPg5C3YK2mQQbTWm8pmeHuuN/KCQ\niSL3WNx0sUAcS775zW8CsF5eMJ8t0KUmX7sGfPXe64jScHZyDLiOiOMeZ+enbO86JTIcbPGVr/wy\n61UVMLR1WTAajkOp194gwdaW9WyF9UGcm7f32doahyDT+fk5h4fPGU22g5JezVcU+Yo7dw5C5bO6\nciU/RxP3/HxduonUT4Min06nDLIJM79ITCYTTo5nRFEU2mk+nyNQpFkckqnKsiSWCuNxxnKdIyJF\nkvYCrlpXFbquiTp4sBTOgqw9q2C8NUFJycnZuZtwXvb29kIRrcVigeq7/UAbVdbrp6yX61CC9uTk\nxJdfKMNkiKIIJSVlXjALGakGKSznZ45TvHvjDv/mv/Pvsr2/HyZXE4hqBnmv59gKwhI2F8EarHGT\nr7kuTd2i0GakRiHVvxv01EBl6xAnsLatatj8fjmNvmE2hGxj2SwQm0wi68syN3Egh522HGLHDmFD\n6rpGosJ7J4nD4quqCt+b9UcgBev1mqWvJHlwcMextfL1xv1cYpHneiep25PSL9JFnpMoSW84Cool\nz3OmFzN2dnbC9y6WSwTt4ra3s8Xt3i2m03Mqj9f/03/yu8yOTyk9j9xqwy998Q2s1ZR+L8pke8Q7\nD95nvV6y9oFxaxP6/R4+bEJVF1TGUuP2eAU4uH0XawWZx5D/4A//JXVdgpJESd9/aMw6zzHGkvpx\nGCc9esNsoyzFYDBgONnm4UePQ5+MRqMwbpIkoSxLt3Cqhm3kuOHdjSSEEGhbtyVrDWA1omO1u+xu\n0W4rJwSRiJw33fR3UYLyhdNaNj+fVD4TihyB34wX0G6imW6tXqk8dCEpKvdxk+19dJ1z46YLan37\njz9gd3uL9959n/1dF9Q5PZmTqih0vBEwGIwoKxGU9tZ4m5t7N/nxT98mjp2yWa5yd54P5CkE2jil\n0CT29LOM2WzG3v6Of9Y5QkjOT4/56psuAWn7K9vcf/c9VqsVmQ+8WASYgJAQxwlKxqzX61BrpaFR\nrpbufYbDCVpXxHEcrJN86SaFMMMQeZfWLYjNgIlVAsL6+se+qYUKQbNG2kxIb0X6gFKZVyGTVErJ\nZLQVyg/k+ZTb+wdESgXl1x8NWRcVmbfAy7Ik9fUpdrddsLM36GN0TbHOWS19hl4UYTAhcHrn4DZv\nvvkmZ4s1C6+QJBptDMpPxl7SQ+uKLFKhTda+oqUU7STqZz2stZz7oPjIlzt1uEqn+iA+dVs2CTlg\nbPtvpZQLWGKCByTE5rPcMW+9NjCVB7rcjld+o+5LFNLLqfndfzfWdAi6irYswWq1Ik4T0jQNf8+y\njDIvyFetIh8MBsQqDnX5hRA8Pzxkd9uVXt0aTwDDcrEM9bjjWLG3NWE+PQv3KcuCvb1dah/Mns9O\nOfvwjN///d/nyUcfune6uOBg9ya68n0bJ3zv23+MoKX6vfv2z3hw/12MrjeyWx0rp/l2T92tSnp+\np5/h1g5CCHJvRR+fTxkMeuiqYuC9u/PplBs3bvmEJ9+PMqIoDXmzuFhLUdYs85LDY0c66C9XrPM8\neHJVVVHXNUVdee3sgtsvZs02rCo/JpBYv4Fy4ylqrUmithpi1Nk03tBQjhcOEjMdaPJKWO1quQ52\nXsu1XMu1fM7lM2GRCzoF1rGYutnjrl39hJIYY1l4K257a0wS9/noQ+caWaMYDiYcHZ0EytZsPsPU\nmpF3kQ1wOp0568rjWjdv3uTVV1/lJz/+GYW3YsoiJ8Ki/Qo+Wy+JsozxqI+V7rpeL+XwdMnBtvMI\nnh+dobXD9BqK3tZ4i5s3b/L+Bw/a+s/S1Xubz50rHMcx63WOStvkj4ODmxTrit0d51k8fXLEwcEB\nD957HCwChyEL0iQh8anzaZqSRDF9nxyRJzlIVzWwCYC1m1e3a7jWDptt1v+8LJxFtrfP2GPy5+fH\ngQIHMBpNKHW9UQtEaw2ydT21MYxGI5bL1tLLV2vy9Zq6KMn9jkA6SRDY4GUsl0sOnz5lqW2ICfR6\nfebzOWt/TpKlyEiwXOeBU9+kqQ8HY4yPCQT8vGr3X1VR5CpcduqTGGN8cbEW2242HHHjywfArWwT\nOxrc/GNcYNuJvzYlH15maV2GbbCayGMNVeXyE9LUVfwDOJ9ekGUZu7u7rBbekixKVosV62VjkVuG\n/QFKyeARCGu5e/tOgNKmJ+cUVUGSJIzGbq5EkUTrKljtTb/k6yVvfe+7ALzz9tssFgtOz08Y910/\n3dzfJYskwkMdg6zH8dFzBIb9HUc/fOuH33eQpMfcAV8vpbOvpVQkaZ+8gl9686sAbO3sARLvgLG9\nf4M4jl0+gS+VMTSS+SqnKPKQSKi1psjL4LVYaxHSVfBs9ruN4nMHkzQQXNxu/BCCx51Ev7bPNAaL\n9i62FSLso9CIUoooicO4qSqNxiJt62k1xAorCHsViM8bRg42ZGhK65kYtt1RxSAQRlHbml7PDZiz\n0ylb4xH/4B/87wD8ytd+iefPT1AyDsGQvd1d7362NULmywVpGlN6Av/p6TEPHtwnzRRF4YNIVqFU\nWxdlcXbBuLdLfzBqWQ/S8XLLpsiOFEwvZnzlK2/y7s/eAeDo2TFf/epXee/9+6GWjBsgCuOzNoui\nxBjD7mSP2dIFrIQQDAYDCh98THsZUkYMh8OAh8dxDNYniPigVSQdCzlE62vtYg1RFMC4tqbHi9X5\nuoE6l/TQ3itNU/I8DwPdmJo8z0lUFK6bz+dorYOCFlKiHUubrAk25gVgGW9POBjccS+lDYKWMx1F\nEYPBgLrouMcGZJQQNTvPCLew9waDsDmBEi6AdnFxwR/8wR/4PnjGarUK2YH/+d/6W+G7w9Z+aGpt\nwNYvqORuQhC47FHVDWR2pLnniwW2JFK2FSit3Sxj214vEaLjVktBnDZJav4b4wjrKSF5WYS/NZDX\nfDpjtVq1xZ+EdRuumLYstDVuy8Re1MKLST9BWHh+7IKG3/ved/jhj75PVycNRkPG4yEnfiPx85MT\n6rqmXC+Z+uDqYO8G77/zDrdvu0qeO6MRb775FQSGtoSwIcsyjK5DPMlF/gyV38wFFZGmGUVd8/79\njwD46MkZIMM1s7mrs5LnRcj5EEKxWCw2CotJ6bafa2JuUlhfhyiiF+rdeKKAbca3C2RHcTu+a1OF\nudMc09rt9tQoeZR7XhzHAfLEbuLqYEL/NCVyrbVY5fNvm+Dp5y0hqCn8A3TSmUUIclqjHF1Ow80D\nt13Tk4cfEamUR4euGNOdgwvy5ZJ7975AvnKKfLlcEseKvt8H0Ap4dXQPq00g97/11lt8/wffZTKZ\nhIk2GAywtkL6pIZ1ueZGIpDUgTq3Xi4Yj8ecT11AMk1T7ty5w/7+PucnbjecqqoZDod88fU3ePj4\nEQB1WSJQTLYcpjedTskGfUxnN+7lcs3N/W0uzp1lv729z/HRjCTJ2r1NrQW0swiaPTvrGtnUQnYn\nobVl2E8d1oez2Oq6RqXNDFVEShFHEdIPTrelXEFRFmGbrSRJKMp1m0ikNcNen63JJCi/04tT+v1+\naMfhaES/38cYw/NT9y11UZLEMcPhkMQXOjo5OSWCMEFdwkRKDqy91VhVBWnaI4p80pRPvinWy8Ca\n6Wc9JDAaDNrCqxYGvT67fefdZGnKar3e2NElSSP6g8xh5J1gZ1mWIXNSCemZMi1NFexGze66scou\nWdbNf1HUTP4XM0mvKlHrvrtZzAzWuoWiGSej0ShYeYm33K219Ho9omDh+Wdply3pbi5JkpRM+WzI\n6YI//s4f8fbPfhp2cpLKkEQqBEQB1ivDxfnzECPYHo9QSqDUPvOZY4adPT8iS2IObriYyKCfMJu6\ne/7ge98BYLlcEEcR40GftPE46gLpYyUAxWpNlg5Io5ilNwyePj0GK/jGN77hPkNFfmedZicjMFYw\nHE0C5bBpk01xSlrXNnhJrXJ255Zl6YLTVcsSimPVLtKhf12spYlLWc93bRhk7iRXDE3SnbuGQCnG\neUBKOWbLL4KNN/KZUOTG2pAKrFTseJhSoVRTiN0F8mqrw+bLUZSgZMor914D4PRsxrEu+NIbr7Hw\nEfTXXn0FbMVs5hS7Bba2dqhqwx2/f990eo6uS0xZOG4yEGGYXZyS+KDdYrFiuVwync7C4Hjti28w\n7A/IV86COM1Lbt7e4/13ftoWi69rTo8P+aUvv8Hh4VPAZZ2BZHvfBXDiNCVL+xwdHRL5hUMIxcnp\nBXv7zqo5Ob4g7fU5Ojwk8pOvMhohNMjW23ABoxY+iaOESDiGR7V0SibPV1SV7lACXRXJOI5DZqOp\nHKSwv7eH1n6fwTLn+KjNPt3fvcFsNiNLsvD885NzxtvbwTUcT7aQ1jEyEuXhn3HKeDBkb2c3wDLr\n9RphDHfuOAtdqoTvf++7bO3tMfBZhFJKbLFuKWRSMOj1SQZD5lO3KEels5j6wwEj74UtexkYtx8i\nwHs//ikXFxcsl0sSnw1549ZNtvd2N+CW2ivMAEG5yKbb0KOxLK0rFdwgLUq6xXTT0haOwidVxyXX\naN3dOLnLK2+OucUgpN6jPNyjSZT7lr3tIUpKrNVMRs4wGPX62Ky75VyzAbAl8/0rheWjjz7i5Llj\nc33wwQc8e/oQTE3qx2C+mnN8dsz2ziR8ye7uTepUMvBjwGrDcrkgTRUL72F+4ZVbJDJm7auLKjRH\nfhPx3DNrRr2M+WJKNhmGCjBVkTNMh8GgO5sv6GVDVDLi7h03Dx54Rd54Saenp/R6Pbe4eaOgyl2Q\n0spNr6jrJTULsPNyPCOl1PT7WVjsau32iI3SJFSkLKvce02t2lTCOkiu299YqrIg8DeqGi1rGnRV\nCUsaK9erfg/cuqyJlMSgWybEdbDzWq7lWq7lT498JixyIWSwtMrSgK9k2NQTFkisVGRJWzMjF5LT\niwsSX9hqa3vMejnjJ+++zyB1K+jp+TmjQcpg5Dmm1hWw0rXl4sJZcXVZk68W3Lt9QD91q/obX3qd\nqNfjwUMHhzw/OuG9Dz7i61//enjnd372rtuk2O9ecvfGDU4OD7l7cIP77z1w964NH9x/h9/4zW/y\n7/+VvwzAP//nvw/AuYdNjI1Ie32WT1cMfJJOL+uzWpSB41rWkCawu3eLZ08cHSzr9bAip9A5iYcb\nXPJEwaoDD6RZFvBT8IWsEAEGANB5TtYbtMEcJIPhmPVqFqyKLM4Yjyeceojk1u4tpqVhNc9pXM9e\nOmB7tBsSoiKZUuSa1SxHDLw1aCFflZxxTpq49753755LiGkycGO4//ZPuH3vFZ76Wi9Hzw5547Uv\nkngPYD6fc3Nvn5s3b3J64trkm9/8Jka4XISP3nZxiqrSjsp56lz/f/aPfoder8dqlTPy8NbB/g1e\nvXUbbQXWY/Dz+dxvvOs8kjiOGQ56PHr0KGxJZ61FqZjuphVp7LHwToaiFcbHM9x0Oz49YTKZsD1p\nap8cMtna2eDJP3n6FIsm82002doCJOvlGh02U9EYYcgSyPx1g9jlFbz37ruhf9/+ydsYBPfvP/Dj\nKWdVLBC+4Nx4POajD39KL8vY33YWeC8SvPnaQfAMAEQ+5/zwGV/9i78JOEt+fXHGrddeQ+27oKiy\nCmEM4x0Hk/32t36XyXgMGHZ9IPXho0fs7++zXM7DRsOj4cAXBHPfsTOesF6tKBYFK1+1cGfHtcHv\n/8H/CUB/4HbtimJF1VSpVFDXTV2bFqZyiU34fvPQlWhru8eJpNZ5KMgFrvZ7VbebdqRp4mmkVWiT\nuq43NjyXuDiNsmA8TXPQS7EG1iu/iXS/74eHcaVV8bX06xIlIG42lL+0QfbHyWdDkSOQqtlfT9Ok\nvivZFm831oJSYbsma5sKZc2+jyXrqqY2gsQrlpOLKWWVsrU1Ds9KB0MW01XIBOslEXtbI3a3tln7\nYONPf/gjXvvKl0JCiJSS0ljefe+DcJ/9vT2sJpTmvLV/g36kHO/VLy7peEjaG1CVOcfnfhuzZm9B\nj3Ou1wXrvGR7exuLL9dZlgyHI6Ry3zEAlOzz6MEpsWcDFFWOFSti0UIrSimE37MQCFUR67rGRm2l\nvS5kYKxLjFkXOaXH1pfFGhnF9HqDUADLmJpeNmI4qMJ7jwdjemk/uJXrYkpZ1kR+Z5ZBNqI2mqKo\n2d31hYeWU/LFGlNPGB64yd8fDFAWtN8ibjqd8va7bzNfXARGSj9W6NWCo2dPXN8+P2Yx2eHi8CZP\nnri+/PCddzEC0sEA4++1P9lhNltQlm5x2dnapixr+lIx8YXEios57/zwJ5TGglfkZV3x6uuvEft+\nKvOCkgJT64CvF0VBrERgxhhcJmbXjW9jPgbjV6rbBzdZLBah4JvW2rFN8nUI+DZFxprM4bxYYSpB\nWVb0VLOJhCsZ24sjjA98vvvjt/idb/12wNGFBVMZilzT8zDVfLkg7UmenfpF8vQxr712i4/uP8CO\nPLSyrvjKvV/ii2+8HsbXuz97GyYDnj90wcfzo+fcvn1AguDPf/3XAHj65BnTiwsir0QjJRgO+0hh\nQ08FIE4AACAASURBVFxqMh4zHA7dhhCdjTtMrTdKFLgMZRng1HXRJGG5b20ChkK4PI+mLaUU1FZT\nd0oQCy5xwIXxlSLdr5JGuTfPd4aFtLYlOFgLvrRAc6ckcrGOJpYk/BiQqr23sG0AFHx2s2qIBR7K\nsw6Lt8IStvdod/T4ufKZUORNwgSANS6A5/Zn9KulkGB9UkuHHtZLemR+G6ja5I4uFAnSrKGaLVgv\n58z9QLBSMRnvIU1E5C07IS2VNvzs7XfJvLKbrS7Yv3cH7THFyljuvf4GN/duhhoS/+Jf/CG39vdY\neRphGifUZckqX7U1vI3hwYMHrPKCvVs3AfjmN5w1s/RW63e++2POz88ZDDNKjzOaWjPZGoPw9ZuV\nZDWvOTw8pMgbzLMGWSPolCwVhiiKNrBBF+XfojB+41utqarVZjqycOnYzcK1XC493h5RiSaz0u0t\nGs5ZrOnHPR4+fBieV+ia1954nXOPWf/oJz+i3+9z+/9l701/LT3uO79PVT3b2c/deyObzU3UaomS\nZia2ZzKyPAIyMRwgmATGZAC/CDDvEyBI/oAEeZkECAJkXgSYwJkgeTNLJkFmMS1ZtjZblsRFpEg2\n2d3s7rvfc8/6rFWVF1VPnXNJyZLtzIAGugSBt+89y7PUU/VbvsvNG2GxG0UjhHHIj7k32i3ryulu\n+MMxpuH2nWdIkjWTtRNnqI1z7ff7DIdD6roOXqfL5RKkoMhz9rZ3/PlaHj36IHx/nucsZnO2R9sc\nHOwBrnF+8dqPWJVVIDcZY3j+2RfoZu58zycTkILd3X06vh6LNnS73WA/GAmB2YjE3bBOrnQDpmga\nB7FFtuSyjttcUaFRLSPXeGshqHVlKKqCbprR8YxFUzdgLN0s5Xf+138IwNZoyPv37vLZz3wq3N7C\nao7PDxlrR14bjrpce2qX+0fvALC7v8P5xQl7u1u8d9f97rOfeIG6qXj99ddpZXqstdy4cYNbN592\n50jEarXiWz96jXfedhIUn/zEp3j5l14OTdhvfPMPHLrG2NA7eemll9DWcHZyGhjHhZdxbeel1l7W\nAMty5jcz663bmjWsELhixwaSKE0RG/Z32hikukqtlzICodGtjnjby/B/144/7/7W6ju1xu9i/Yw5\nGYdN71gNVpMl8ca51Ei5lup2/127PK1/h39+bTivX3Q8qZE/GU/Gk/Fk/CUfH4uI3KmJrfHJ1moc\nHchDv6TBIImRoVygZEySKraGrs55Pi2JlLhK7beSKMkoqjY1MiznJ3S7PXa8Ow2m4vT0lH6a0vF1\nveHWNpeLORdzl0JP5jNuas1g2Avqi6enp2yNx0wXLlo4OjphOOzT7fXZ8tHgB48OGQ7HNGYdaXS7\nfYes8LlYv98njmP29/d4+21XIuh1t6irItRUpVJUVUGSZOSrtUemO6e1s73WGqM1asPVpGkaMqWw\nei1sVZY1mzA7gaAuqxCN9Lpd0jhh1ayFl1QkiSIVItuqKOj2Mq4dfCJEDgbY2hpR+/p7b3CH3qBL\nFEX0++7aylShDFRlGfDmTVMhLKQ+9UzTFJlGLFZzWoBIUVTkixVF4aLfNE6YlyVHJ4+4trfvj6ly\nUW+kAnKprjVlUwdqflGsKOsKFFQeW356eMZkekmUxFhfqouzlD/61ncYermHw9MTBqMhn/rUp4Jm\nSgsfa9E+Fm/PLuwG3t7QGH0FUraYLx1KqKVp1wZTG3pZJ3ipLucLEJbGz5u6ajA1ZFmENu66LRZz\naAyJaoJG+OHJIVkvo/AZmMAiUsW1WwccHjr0yGj/Nv1Bj5dfduWQDx49oNPpUM6WPP/88/5+JwgV\nMeh2A7Tu5s2bvPveXc49T+Peg4fcu/fAiaV5n8nXXnuDneEulW5Fy0pKX1KJW4p6krK4vAScyTls\nRNdtPR6JUhJFxGrl3l+H1cpn5coGvHcQN9twgGodmJRQvry1aWTt7lX7evd3u65m2I/CFq11a4vY\nUKmMpNe1b0s71kXkQqz1fawxxLG8kim3WUULC46k06CP04i09eP9iBzAzx4fk4V8gw0oLdpzmoS/\nOSqJkUYQJet6mTCKKFX0+y71PZ0YB+o3OuCK0yShl6XBeg0ki2VJ2WjOvYO70DXlqiRKMs59SSDN\nYt568y6r9oFNMt555z0uTidBDGk8HiMlgQ16fnGBVIqkiWgnWr5cEkcRq8U8nF+v10MIGcoBny4M\neZ7z5hs/YuEX6e3tW8wuV/T7roasNejGcOe553jTk41kHCFFjK1t0JWoao3QtcOj4ieVUUyn80CI\nyfYykjRd6zkYx2Ccz6dM565m+9TNW8ymPWbzM1TsziVJemhTEyVrFunO/g6v/fC1YHi8zFf8xtO/\nyf41V7J45913Ob0849qN6yyLVhxJUJYlda1DCplGMcKuHejLuiDrRORVsa4rIsnn+Rp3KyUPHx3S\nSTPef/+emwPnE6wwxN0szJOtrR06vS6lJ5pk/Q7aarTSnJweA3BxPkGi2Lm+g8Q9YMPhmO9/81uM\nd925HJ+fc+e5Z/nMC5/GtvXNKMFEyYbw0zogCbhmjYOUWYvUbdMsxWhD7k2FIxFRFAvHVvUzddDp\nY20TcNyJkMTdlH6nQ+M/pyhyinKFSGtWxs0dHVeYpiZvZv6TDEqmpP0unaG7d6ojePPuqwy8Sfhy\neUpk4KkbN9gduzlXLVakccRqsQyllWKZ040zVjN3vo3R5E1BXTQ0hw/9ccb87u+/wtHjh/5cOwwG\nIzZliWeLwlknRmuzhQZnEK42Fq/GurJmZdblLliXrSROEtoItXY/UnLNQA561q5IuyYk+fKM1WF+\nORz5evFuN4hNSGiwbpOsazACzIb8rpQgRISKY6y/d9Y6R6hWN9lgcdLD62anAZSKUEqQetDFn2Ed\n/7gs5M5dBUDICGE/Kg/a6lFvdodbBiA4YSsRx2jbhAuQpimz5SK8xyIZjndIky6rhZuMq6VTH9za\n2qLnWXTjrT6zVY71zKzx/jXmPoqVPtp86qnbLBczmnwtxBNFEYvlLDC6VqsVBwcHaK2ZTNwimZc1\nCsHII0mSKHNdbFVR1i7SiuOIRq+RJovFgunskr3tO8H6KzDRWLM0tdaoDcZgKwbV7/cpJu6z67pm\nuVhfE2GlZ7lJ+l3f/CtLOp00EFDALeSLZRFwzY0xfPDgEcPxOHzfQA8pioK04xaIz33uczTWsFpd\nlUy1VrBc5hS+Ru7UAtdVvswkaFvRUR0STwpTIqabdNcLuxAsu96dJvfsOCnQwjFhW/U/rTVKxhw+\ndov2YNhDSkFZVaGRWxQFVsPZyZHXz4b5fOmZssLfJ0Wv12V7e5ulb5wqFWON3Yj0NJta4IDDeQuF\ntHLNyo0kxycnlD4jefrmLaqi5vT0OFzfa9f2QdhQj1dKEccZ1mqWc/e+s9Njzs9POTmNuXvX1aif\ne/4O52dHjEZrlxlrBWdHE4ZDH7UfPmK4lYUs8ZlnnkFpy9/+2tc4/sA1kztRQiIVk/O1aNaDBw8Y\nDod86zvfcd9/cU4SO1SU9NhqGzkiVbu5b49GFEWBlIJ+fxDut8aRAOuQTXp0jH/sm6ZhWVYsW3Ib\na6LVlaalr6MHzPWmxEFQxbxaH2//bjZ8agNWZTNT3SBzuWPU68/+0OvbjCBOEmedqOSalGUdwKCV\n5W43fGE3tfnX2UUT0oK/dBE5WNFqjwNWYmg+ZJO7vgjtuCrQvrbYCpUV3OIdLqCQYL2BwEZ7wLXZ\n1oYQoaEamhzGKTA2ekPA/upx/WntBiFEaKI5HeKPTqr2SH7679cTK6SexiKij95os/EaY13/271v\nnZX8dLH8P+uQGyWVDaidvWo+7KJuEzSs3WtclNIK4EY4KFirr27bc7fQTmYH6Vt/u/bMPaMEjX9g\n9cYi2qbMxrOGTThWENIfZ2vcjUWKVqlwrQCphAjNbWn8unAFSSA+EjUFc4527ghP+Nlge7bzy268\nxwbjhU01PUOr/SIVLryXCiHXZUgjGhBiQ7WxwYp1hAjuulpp0cFUw5GaggG3f1LWjENAmI9sSuFY\nw3xpyxXe4IGfVo6wgcX64WfXGBuee3dMcs3GlMrNDyuvRNIffibcHDRsPn/OcEP+1Pf8rOE9Wv7U\n92xuIlc0cT76aUj1UQ/Yzb+Dnxs+3QkVnZ8i3fCLjCfNzifjyXgynoy/5ONjEZEbCFGKcWLYV2B1\nrrTi0o622WZrF1UFF2xjSLMEITWbOPqmadAbG2NvYIjTDiPfoEpiha0Kqloz9GWaVVFikRQeIlge\nn9KkXfLVCuFLK5984Xny5ZKhrzsuFgvKsrwSNff7fe7fv894d4fdfQc/7GQ9QIYUXghBUVSUZcnY\nN+SWC1eeaTHFx8cu5Z7NZmvFQCymuSrypJRCCknk1RCjxoJQdDt9FE7vwjRO1Kq9bsJqsqyH0ND3\nxKkHDx5w/fp1+v1uIBPFcYyQkqptvukGhbiiWRJnKdvbYw+dg7xc0e/3uXXjBR4/dhIF3X4fIRTa\nmpDaV1XlSBVttCMEg+GIsiwCPC2Wjj7fljqyrMve/g3euvsO+zuujr3lA7f+YBCIHU4zQ4csbbFa\n0Ov1MFgyX6bZ2tpmfjFjcn4RIpuyU7Czs0dLvu53uox6TrRs4XspSbdD3pQkXhAMpVyJbyMDassp\nSiqkP5fz8zOOj48DrloA04tzzk/PWHpX+YvT61hpyLJ1aaVpLLoRTKaO3PT++3c5PTsEpXn5y65x\n2UkjHh3d53J2Hu5LrzcgTiWlr9keHT/k+EwzHnuV0MspX/nlX+aPv/dd9rYcRHF2McFqQySiUKFY\n5Cueefb5QN5TMiZNMwaDYZgHs8mE6wc3ePrpZ9x3HR1xdHSCBfb23H3a3ZXMF0uiSFHptizoasLt\n9aobTV0ZposViVdWbIXnAlT5p0TF1lp3P8xVlULE1Qg5iqIrlpDhZRvCce2/2991Op0PUf2vYtnb\nz00SByEMmuPGYdbDnPQ67Iq1uUmWplgs2poNKOMvPj4WC7lAIPxJx9KVQoxRIZNW0ikGbtosOUxq\nTeXrynmek6R+8fc3vCob9vdvIFvvTStY5AWHh4dBiL7f6aGyFCUajG9i3X/wgP1rB/SGbmHN65r9\n/QPOTk7DAvzo6BBdlgx2HELl8uFjkiRhd3ebOFl3wm8/fYdltXZsmU0XgARvaXV9/xaRkH5xcbXe\n09NTru0/GxbyTfx2Kyx1eq5R0vr01H9fHBEJgtgXlUQgOD87C82/4daQ0WC4VmbDLdLz09Mgv3tx\nccFLL73Izs4Oxm9c3V5G1XRZ+oasbhpUEtNR66ehqgoOjx6ReZGy8XhMXVfcfedtdvx1quqC5SKn\nKKsgUbuVJGAlK79pnJ6fEJcRVVWHxb3X9SJjvtewKEpu9vpEcULS8gaO3OIlpGTgtUfqumYymwbX\norKpyKuSIs/pepOKYadPvlzRlE1IdcfDEbPJZah9qiQljROEFQHrnaUpRthQNnPKh21pqSVcedcg\nXYcyzenpKZPzS95739W133/nXU6ODokjFcoIy+kFlmad70tBXWm0JjS3j09PuZicYmXJ5cKdS7/f\n5fmX7gQUC8BquSLrdVj5hvPLX/o83/r2N7i851Ast25e5/Hjh8QyJve8iF/69Gd449UfM7u8RNg1\nA3RVlJiwoFqOj0759a/9Le7du+cOU0ouLy8ZeVRYVWqyThchBEsvQby1Bb3ByGns+PKWEpYoStYS\nwTJhWVmiqNkguF0t3bRCV0KIUFswfiM1Gwuiw6OvF/UPC5aBX4iluIKZb3+/6UoF68W7HW0A1x5T\n+/7N5qwxa8XDxrqSY+1eCOABCC0ZsmUS8QuPj8VCboxh6fWUI2l9DdCEh7iuHMNLiipAv1LVweiG\nd37iqMj9QQ9rNDtbY+aXTgwoSTIiFVN59iVSsZiv0NoGqjedDG0tsYpZtBNtexeimK5vzlw8OuTk\n5IRYRUHBLF/N6WUdFr5p+sLzL1LVJd/67nf4K190PqLXr19nMBgwSrf54FGrmy4Aye51t7CdHp2x\nvb3raqp+0ej1emRZxsmJkwuNVEK3KxAmCRMsSSLHZbc2LFIiMpimpvKQprIsSWLF9ev7odlaLAu0\n0VcmucKSJFEgJ2xtjXj11Vd56qlb3Lvn2KxZNyXLsrBpaGOodcNiNqet8N2+fdvZu/nFf7W/oK5r\ntLbcfdd7lm6NWawKfvVX/wbf/8GfuM/SGqxkMHCblG6cWFS3k/l7D+fnE/9QtRGuZHJ5SZylQc71\nqWduI4Tg0aMPwkOzu7VLlCb0fbYznV+yvbXFcjpl6qn9eZ47zeh07RqUJAmmqNZklOaSd976CXc+\n8WIQc5MWTNNg/NOf9tJApmobqYPBCIylqHO63iXqztN3+NKXvsQrv/evAfjGK7/LbHrJ3s42t7z8\n6xtvvAZC87f//a8BcHR2yuvv/5jPfuYLPDx2Dcl7999h79o2x2cTSNxGf2v3gK2tLba2HGxSITg6\nPOPk6ILjE7dwn10eY6l5+rbTB1/O57x/7y4vf+5lbuw4KOe777yHUhHbWzuhV/TWW2/z/e+/Tu03\nsovLBYPBgDff/Akrn0n0uhmj3oALP9/my9U6IPFEudlixXA4xFgVGJllU1PP51c0w2vjNoGll5yu\nP1SfviKG1ZpGC4Wpa2CTXSswdp0pt4JZAhOiZgfjNYEN2mqRG2NCFpzneWBGb0burSb6ekjKqgrZ\nJBiiKCb2mXJTlWsrQq/+OJ/PGfR7pGkasps/S2T+pEb+ZDwZT8aT8Zd8fCwi8s3hxPktWlcoD2lK\n0wQhHBmlhXrV3mC5hVkV5ZJVfonROqSVq2VFU+lgumqFSw/zsqLwML7ZckU/i1mslvR7vv5uLdVy\nSde7h8RxSjfrOLOF1sG80wO5TvXdrqzJsi5LX0Z5+NA5+kTeVxFcuQdrgz+lUsIRESB4IXY6PQfZ\ni12UJSJFns/YGe8HrZaqqRGyQRtCBF7XFUbXNHJNTTZYsAIp1uUeqdc1PYWPaiRBpMwYTbfb4cdv\nvcmNW05aVmtNbXQgKfX7Q5q6vOI0tMoXdLudIH27WixRSlEWFSN/T5SSdOKISEg6/r6obgpWcOD9\nVwe9PnVTOHKYP98sTtZ+m0BeFlgLnU5G4ktJWZZgraDXG4T6c57nVJVB+ZR9f/8aZV4gpKLr4ZY6\nd7rrSbwuN11eXiKEIPVRZCzh8ePHfPMbX2fshdK+8OUvknZiDi8ctPHm+Bbj8RBhBPOpl4XQhjiO\n6aRpiLCiKELXNfffc9nO+ekZd565Td3kvPrqD933T85ANPzgh17DO8/p9VN+/Oar3H3gxK9qU1DU\nObsH2zTWzbmsmzIcD8I9aDxK6zOf+Qxnxy7Dm87O2dnZ4dYNF5EvZpckIuGFZ5+jn7rnKV88IEu6\nvP3m2wGKU5UaY0Twzc2yLuOtHYajLaTPJvd2txl2O2Q+m93e2kdKh0Zp+y0XF5csVycYREAuaeNK\nEa2WmzHeeccaMn+fIq4iOqy1COPgeqHcE/68Ucu2EsTVUocQAmuaUGJsPVkbuzZ6CN+xUf/+KCpL\nXHkGmqahLEukFEgvgofHkSv/7ER6DaNudeTTNKYqCySGXe/KFG/Mx583PhYL+YfrVcYYJ77Upqxp\nJ7C3ur7xMa2m1I0JtWNtCqqJYwvujNzvmto1uyKzXsi7aYaKsmCXtSpyhK5Q1OAXcmMM8/mCNHNp\ndWMsTdlcURFM0oi4k6wbqUKhbcN4ayewD5ezJfv71zibXDDedU0kFQmEVSQ+pcu2elghWa5ydEtG\nGSXMpznDxJ2rEIIiL0mvZ+s6W9NgVIWwEU1rLKErmqoKQk+NNWR+ooWFW0Q0oglNJcSGE5M3G+4P\nukgR8ejRI+7cuROuwXvvvRcmY6lr4iglSUrackSVF27B9t+/WsxcCl2V5G2aWSyRUUpd5OT+HhST\nKQbFcuk2zrPjI249dZ2qXoU0M44csauq1gbZIorYGY/RvtwVdSRWKbIsC+kwSJQQdPzCMl/N2d7e\n5ezYYBN33M88fYf52QUP7n0QzqXX6SKlZHly7C+ThCTi3bd/QufQMXCtbSAW/Nq/9+sAPDxxbkTC\nCq7tuw1wZ3uPi4sL5vM5Q38M3/ved3n/3l1OjtznHFzb56133kBiuX7DlTbSDkhheP99p33SCMvF\nZMpXv/q3WDZuk3jhpRd47cc/ZDo/57Nf+LQ7v+WM1fuL9aJlJboUpHGPM19KGgy63Lx+g2N/Hrvb\nW9zYf4rDw2N2+n4OdAf8+I23efzoGGU8RFAKdGNpvHdAt9ul1xt49T93zx89OmSapUi/SXe7fV9a\nMaGMkqQ5TWO4nM5CX8p4CGNrjtNY6dzprcb6oM3KjxaNhd8APAcQbQxpt7NWOHSvQnDV3tDBWdWV\nEkkkBERroTnXpFw/O878vPqQ1dtVI3OtNZVpiGIZysDWWpS24Tm1Vqzhsb4ocn4+YTQaMBwO17j1\nD9Xi/7TxsVjIHTvKnXSz0WTY3BWtNVeaDHGUUpdVsN2qypKmKihXEVXH7XLdTspiWVAVbsEwOFyx\nTDqkvm5X5gvm8znbo27AoWutscZQe3LGalmgvYVU29iKE0Vy7YDYIxaSrMNy4uqBLWNRKcVqteLs\nzLEbAV8nlS3Ji7qy6LomSSLqgIOXdDq9KzU+1zFXwZvQ0Nbx1kL7hbXuOm5MPBEpkAKlPurV2Q4p\npZOR9U9a1nfMSBEpZp60c7B/jVVRhmZrmedEUUSWRLSLX1PVxFGE8F6J03KGFAOkNdTeVKDBsrXd\nJcsybt5w5h5V04CNyHzTspOkJDGkiaTXcb+zHv++8nMgiiKGW1sIRFhYqqpBCOfYk/lopqk0Vq8d\nXHa3djk6OqLT6XGw7xbN5eWMuNPluU+8hHEtKB4++IA0jjn30Xba6bA33KeuCspzt7m8+XpJbQ0P\nj13/45e+9HmeefYOwihqLxFw/+67xGnC1mgcGmnXbxxwePQBp2euZt3rdXjhhefIiwVT76bzwvPP\nIqn5/o8c2qesC3b3xtx5/jZf+GsvA/DP/5//mxdfepG37r4RcOPT1ZzJyRmlzyaFlWwPDyhmGulN\nDPrdAbvbeywu3XcNugO63T5nj0+wfv/rpgPyxQph1xZ1UiRY2wT54eFoiyiKmK9yJ3uAAyYUeYVV\nbXYHi/kKK0ywYFwsViAkIoqdVCAuG5QoNB4jb11dW1sRGqIfxnBHQoJyz1krrmYBWiu91jTiQxyE\nzQW+JWCBB1psMDtbi7j2mWlt/D7ccK2q6kqmoJQKfBX3GuHcp2RLPhJYhCME+T1ha+eAXidmtlgx\n9ySstl7/i4yPxULuXDfWN8ngrI/aMoq7gIqiKMi81niaJNTFukFXrtzCYrRm4W2nuv0haRKjhLsg\ntYWyKDC1IfINwjiOMVrRNBUtkk6bmkiIoMVdFgVmvggLBkBlG5bLVYjShThlNZ8xGvbWkpbWcnZ2\nxmQyDbu4yyBcMwRgOr1wWivXd5jlbqJXVcVotMNy5dUQjQlKf62yovMglIF4Am4zbKyhq9rrdhWm\n5f4hgqyme5MnbEgRDJKrqiLPc7rdLEz0oii4ceNGiIgHoxFVvsBaHewKhQRhNLpVcSxLpNYooTm4\n7qBnF5cLmqLk0f0HXFz4B3u5whIFyF6xWjLe6iFoNrwnJdFGUymOXfM1RlJ5Zmd4bbPWNmmqmvl0\nFs5j5+CA/f0DTk9PfaMWrl3fp8oLZpfzIFl656UXmEwmbPkFspelRJni4uKcbuoVN1cpcTfhkS91\nHJ895vO/9DIYwa/9u18BYHhjzPHpOYvFjLHfBL/4xS9w9+5PXMMa6HRjOr2U0XaH2fLMn6/GYLj1\ntIvsHz7+AI3mr/+NX+bY27Fpah6fHHLz6Zv84LVXAfjEi8+jkpiFXwwkMTcPOlSrhtu3XXY1nZ1z\nenLBc899wt3v1Zyjx6dc373G/Mxdk7tv/5h+Z0gnK8IcWxY51goGI3ce4/GYqql9lOrmwHgwoirz\nUOpotGU6nWA3ZGyxgjjuIJVoJUuQSOq6WRPXRISQkkQJTGjMX9WsaaxB+sh7c7HX2vjgz+u4GEBe\nZWgK4aL0gDJpGhoprygrfnjRbprmI4SdttTSfk6apk6CwRiMcPNEGuM2Ln8dnQ2cI0i1gI4kSXn6\nmWdJFbz/jgNw/OC9E37R8aTZ+WQ8GU/Gk/GXfHwsInIgCMo4eq2AKHapE7jdNBAtWvhdcsXg1Frr\nBKlwjVKAxWzGcDAm9rjmDMHlYslytUBVLiIf9Lv0h0PmFyfgI0lrGmSksL5hpKsSWXfAmtBMGQ+2\nWCwWTM5ddHR8eEQSAXYn1L+FtXS7fZbLZRC7unnzJhLB3rUNgpAwLJazgCEuc43ta3IPhyzyFZYO\ny+VyIxWUWANOaWKtfrhpZG2ME+TajBhCs2ZDwL91OAkRrbUMBgOSLA0enQ8fP+TWraf4yU9czTZL\nYkqrwQsAAXTThKaqg4iUkFCVBdYaZhOXJZVlQxwJbKO58/RtAGaLHG0kFxeucTzq9yjLJbrRAdaG\njNg72GdvuOf/mZJ2uixm86Ag16ZUdV0H1UQHBWxYlO4Xu3uC9965z3h7xO4N17ScTC5Ikojdawdo\nb2ZyenRMfzig4/XurdbMplOasqDrMdLz2QVmIdi97aLmL//KX+WLX/wyCoWu3efce+8uSa/H3t4e\nuc/elssl771/lzhthdS63H3vHV785PN0/PV+fPQIJS2f+JRTI7x15yZ3nn2W/+a//a/Zv30LgK3d\nLT77hc/xo9f/BG1adU1X22/hh8JGdLt9Pjg8ZNAb+4khmEym7LV+nDbCaGfUXVXeXGSR0xTOQKSt\nS0ynM4gUW144rWk0VVlfKXleXFyg64a0587DNG4OWgj9pCzrIFDoRodSamM0xqwlU1ACIZWbXx8C\nVG+WP4xxUg12AzboUuINnX4rQdsrNXYXWV8tNV6FEK6Fszahuh8mBG3+ffM1V+vornkbsoYgAf6w\n7wAAIABJREFUByJofNb8+S98kXy1YLGcsePVPNe+uj9/fGwW8rZZInAdbK0bpNc16SSueZHGSZiw\nxsQ0zbrr7JAhiqouUL4WVeUVq9WKNPV1N6kcY1LbwChDN8SdjCzLGHlmo24KFqtlwEPHiSDNIhbz\nZcCrxpEkXzUkflIXqxVGxsymC7od97ssTkiTjOFgzENvG1fmBdISEBiREkgZM5uckfTcuaRpzy/i\n68V3sVgw6K8ZdFJKkDHCrIWtnMi9CqUdrKGqC7Ru1noVRjs0CO1K15YpTfg+MHS7PeJYhYdmPp+z\nyFcsFi49tt3OBvvNbcKD8RZNXbFauUJrlkUUVYkSa3eUYdJ3eiEq5uDAbWbz5X2UUty/f99dW6Xo\npAKpLKsWQ1xrJ63q54mKDXleY9Dh+0PT1tc3AdLIiRg1foGaTKYkScZ8vkR54tZwa0zTVMyXM7S/\ndtmgB1KFtF43FYPBgFgKOl6dbjG7ZP/mdf6L//w/A+B0NqGpCrSVpB7XvbW1hZaSolwFh/rz81PG\n4yGnZw4Pfnx6xMHBHtevX+P3v/67gCP2CGmo8KJswrC1t8vf++2/x4/ffQuAb33n25y/csJTz9zi\nuTvOyWfUH/GgvsfuttvwhJUIC5004+LY8Stu3jpgd2eI9pvboN9H9Trky4LU8yuuXbvB/ffus8xX\nQV6m0ZrxeBSae9PplLppEJGi8q49yoDROrj46NqwzJdOF6fteZUOUdYbjq5IILc6PODKIcYalLDr\nwEiuAzt3buvApLUu1NaXCM3V+bBZM3fMaqe5s8ngXCseuvJHGyy2o67rK3OrfR0Q5onRdcCba7MZ\nYBGIVW1pxZVVfB29cc5Gw/4AYd069NP6WT9rfCwWcgsbolkRaEciUb5e6SaOoDfok/sHu6hyyrpA\ntepwSYyMI6yuXYMPiNKY2XKJKsrwPVu7u4zH44BaMbqmLB1552DPIUvyYs7Dhw+JPT16IIYk3ZQ8\nX4aJVpcFSkluXXcQriPrIHyrxZyu30mzrMtiseL6/rVww8uVY4Edesp6Uxpu3rzhmqB+gm5vb7OY\nafoekWOFZDqbsLU9wKOViOPUKx9edW2PhFxrMwuLxS3aljba1ggfSbvXtO9du8O787QI62B44Gp/\nR0dHoblbNw1ZlKCSJHxWfzBkOrkMVGQVJ2gjnJ66D9q0MpSrinRyyTN+Ejsm6tqJvDPo0e9EjLdG\nAX1yOZm6+ytajXZHsFBKYWLtr3eGEILGQOXlha10D3JLwNjf3+eNn7xN1TRBdGw2n7K1NeL69Zsh\nIq/rmsuLSdiUelnKrevXuDg7ZTWb+vu0y2/91m+FJl4URyRJgkRR+jlXNTXj7R1WqyIQvP75P/9n\nXExO+exnXnJzoKn5znf/gPFowHjoIulIWazQzL1kLInlX7/yr7ACKusJX3nBzes3eHj/g4AsOdje\n42TngI5vSAobYQr41b/6K/zhN78FQDdOyZKIvW0XoS/nU84nR2yPd0JT8PLyklpbhIqC5EIvTdjd\n3yOO3By4OL8EKeh1O5S+uVs1tUNbRC313nqILQx6rrYeZxnaXqKiKDRpYylBE5AedaOpTU1tGlY+\n41KJi4bbjaQl5jRNQ+nvb2M0SdYB8VGRrvVTYlCRuiLkpbXBbOjGJ0lClmXeO9j9rizLkPW2MGQp\nJCpWQbitrmtq3dDtdoOcNCiPupP+uJX7WZvA3M0ShTWwuJwGy8M/i3jWx2IhxxIeNCMAK0jiDh3f\n2Ms8Iy5N09DsO3z0GOI1DtVIxclk4nXCfRS5uCTKstCgAxdVVPk0wBbnsyVlXjGZGNLMXY6nbh5w\n7eY15nP3EJ2cnnNyfkqRLzG+YbG3t0O+XJF6dMT+3p4zm9jaZXruSgTnRxfOi1MYnnvxOQDyxRys\nxTf1GY5HXF5csMgv2fcNwbPDY/YOnuaDxy6CMlaRpjFCGDRr6VVoUEIECdEkirG6ofFMRyEso36P\ni/Njkqjt6tdYmitInu29EXIhGHpMfqRirPYQSz/5OqMOP37rbV580TfIqpLp+RnTxTIgQhoEVkXk\nXoujayXD0TbCShr/O6ESVCdmnuf86PU33PdlHaDhuZdeBKAuS/LllFVeBGmBTn+ARgQdmbLRJFlC\nXpaBHbdY5IB2EU/7gKQpMlbsDr1EQNNggMFoGEy5rXGa9G/9+E1aaInWmuvXb3LDwwgnFxdML3Om\nlyuu7x346wtlrUk8sqbUjrEnrEb547y8OEcmCePhKETkk4nTP//BD34EwLX9PUbdIW/88HUS/2Rf\nnF+gZUU2cp/zN375r5P5LOi1H77m5u50yi999rP83u/9Hh2P+Hn03iP66Yj3vdGysJL9nescPn5M\n5q9TWRT0s72ghY2xHBwccHJ0StzaqRlNlMTIaI1aiVRClnY3otaYOE3oJB1WyjVJu52Y1WIedHu6\nnT537tzGKMEzz7oy0Te/+YeoOKIxdciMpVIkaUbSKkNWNRERkYTKmxZrqa+UMhqzprMrzyWQIqLS\nFbaxjDxTuNfrMZ/OrsCHbSORcbyOqD1bteV7OLllG34GGAwGVHWB2Wimt+XdtgyslMD6YKrV4DfG\n0Ml6QWfcebTmYNaY9KPDD9gajeh2EpaVN0XZWLd+3njS7Hwynown48n4Sz4+HhE56zqrEBYhHDa5\n1aduGuNEZupqnebULs2pfa23ERYjFaWxIf0WaQerNZ2uJwRZzWKxIEkSCt+Q2x4OUEpxdnZCkBoX\nDVtbI0cCwQlN1TYHDHgs7qPH92lquPAki0g4y6ciLoIgV5kXYAUXF5f0HjnM8MHeLhKCONByPmOx\nWNAf9tgZu+bbYlWSJFHQ3cgrOL+8x3I1JYp9cxcQIqZqGhLdwqwaMA3Gp9mmqWikJI2TkKUgtIsa\nfHqOFBjToE2D9nCtLM7Iq5yD3V1WbXNVaMbjIY2P4gaDgfv8Zn1Pojgl6WiEz1KsilzTWsWhHi0i\nBWWDEet01BqNsZbCN6yausZK5chKAf+eOF1xH7HqpqGsGqqqwXo2SNNUWDSSNRRRCUljNGrDqDfJ\n0iuN8k63i6lKivYaAnEUU+ZlsIPbGu5y8+Z1tkdbHD7+wB+Ty45bQTTZniMC6z97vL3NcDigKIpQ\nphr0+qzyJaWHlxbLgmF/RCRh6B2vnr51iyYyJCPvnGXhN3/zN/ne9/6YbscRxcbjMd/+w+8w7o/5\n7re+69538xma0pCotrQiWUzndGU/nO/ezjZ1WRL5Z64pK6xZoqsyRLtFUVDXNd3e+n2tTeHlpeuT\nVFVDt98j8WYKgMPxK0JZIYkVTdPQaMvjI9cTuLicoBuwitDINKYVudrQETdtP8pFyVL5rN3/vS2N\naGswvpDfGEO/32e1WjGZumfz9PSYTpqxu+uyMokry5ycTdZYb6+8uFkT11pvHBdYdKjLh2MUAiVs\nwL+73204RfnXWHTo7zVVgW5qx93wEfnnP/Npzs5PeXjvIU3dAhquEiX/tPGxWcivDCt9rW7diMDT\nc9dNurU4O7hGSoPFSolpETAyRm/UwZSQa0umDbKRZENFzX+fFZueeQaEBrHuzgvr/h8Yksa53AgU\na31Mx1az9mc3LiIp3fezuZmJQE5af6EBrk6Qq+y1jZ9FS0V2mxfCbPzOuv9vmG+Ec7zys8e5ttdJ\nCJzZQPtvgxHGXf/wMEgau74nWjjkgREbxgNCum8ShOsU3OVb2eIr59O+pkUsbTZYr5pkGGPw3iGB\nMRfev75N7r3yKvrAXU9N5M9PWC+KZds5oYAIbIRtmY7Kn6tdn5t7+ya2f/3dV+7VxuskCrRGolDt\n3JERxlZsaq1++HOsETjjBRFeJ217DJvnJkPPY/1+g/DnoSTIdia05y8Exj9zwezBtkYQG3O5VXfc\nkGVuQQLr6+qwVR/mikghNiagd+OhbWRuXCt+9rC+ibo+xo8ufx/Gg/8sw4erphmbH9CiXz76d2G1\nR9d89LPaR97h2nW43lKAbZ/5tleFRliDFDZIZf9bW8iFEPeAOaCBxlr7JSHENvB/AM8A94D/2Fo7\n+fmf1U4gf5LGruF/utUSqde2bUZgxLoTba3EVII61xjlWWbCNZL6PS/pKQyTyYROEodmZ7/XQYqI\nNI4pfWPr9OScui7Z8c3PYb9DFD1DL1mE4/3hD3/IM888w2zqIvu0M8Ro18lPum2XvbWeE0w8+WVn\nZ4vGGBa+/tVNUqxQCCmZz12d0Vjl0Ck+EqnrmmK5Is/yEH0qpbDSoEuDNS0RxqD8/9wH4SRX9VrD\nAqSPdtsJhCMF6Q3Cgkyo6gWDrW2qxt86Kxh0BxiPdMj6KU3ZbBgRw2q1QFoTWHYKBwXr9/qBkQqS\nMqtcbdnD76xQWNZNWiMlRgiUlEFvR3nIVptYNE1DU9UomW4sUBqsQQoC0iHNFHEiNtAFkl53DLYO\n7xv1e6yWUFTabdjAsJtQFTXTaWt2LckuMgbdDpEn8gy3+4x3tsMClagORVWiAesVN2MZkc8r3nvv\nXd7x3qI2ipAqIYramqml3xlwfPSYk9OWEGSpVM6X/rpT0nzz9Z/wD/6n/4XJ5DwsJuPxGG0bBsPd\n0OBPux2ODz9g1xuA4xex0WhE5Ruwl+eXJAdbFD4oKitLUxfM5zm1NypfrlYURcn29jbG3+NOp0NV\nVcyXLrOQkaXbS4gUISLPkpgqrgMKrdGW+SrHYJl6OG1eFAwGTgWzBY9JqbBGhQ1Bm9qhq6QLdtyp\nuBZj4GUK4RmhazKbtZbf+I3f4Pe/8Q2OHjlAwY1r19Fa8/ADx8CNpKSqKvrDrRDtC8/glLFHQLGO\nyGnW7kfG+HntF3fp3aZCECgtCOmYnC2AQ7q52+q4G2Mw1iKsDsHiU089xdn5CfPFgsSDHuRPcWj6\nWeP/jxr5V6y1n7fWfsn/+78Cftda+wLwu/7fT8aT8WQ8GU/Gv6Hxb6K08h8Af9P//A+BrwP/5c99\nl9jQWtEaaQ3gaq1tFK5UROqp9Z1Oh9qY0FEWQlDXtSMwWBd5REqihA2EEeFdsy3r9HTttr5F0yoL\nViXnF5cBmzreGZOlffqjbjjcv/N3/kO01rz26o8BWC5qdrd32NvZQrdaI0Lw6NEjut1O6Ji/++57\nCCHIztx5fPqTn0JGMWdnFwy9O8uNmzfo9oYsffS7yBv6/SGDwSCgdpI0cqmvpxKDh2N51AaAUMIZ\nIWgbasZG49LwjWzQCkfRj3w9ukX5rFarda1ZxdSVRiTumKbTGUmcMRquYwEpI/q9LAibZVmXNErp\nZL1gGoFQLBZLam1DNGYEWFRACTVVTT9zZhNtuaWu1x6f7ZwQQgRcO0BVFAhhURIa7+5UlymzaY5K\n3DUaDg1pnFAUDaZuadUOQ53nOdZH5P3eiKefuc3RkYMMLucLDg8/4LKb0ggvrzCf8O67b/OZz38B\naCO4NrN0F7jT7XstDsVLL30KgO9869vEqeMXAGzvbLOzvUVRFOSeAJVkEU3c5fTYReh7e/usVgVK\npcHI+rnnnuP0+IzLy1n43ueff5Gd4S6zy/V1eeetd9C1YXfkoI1xHIMR6MpD5grDdL7gcjINUWNT\n10gEpjGBrFfXNUWxCtnz1taIJIlo6irMOStcttjqyC/mE+arpSuB+BJEHEV0Oh3myxzZQvSsE21r\nEVDWelcnYWl8OVEbVzALhCB8WU4KYj+hlVL803/yT1x26yfY+fk5wsKBdygCt34YTTC7MNY4al0Q\nunLYcHHFzKby5LerZTkpZch4lSdmWGNR7fIqncro5uuVTJGsNYDee/9dTk5OyIslnbGbF0L94nH2\nX3Qht8C/FO7O/s/W2n8AHFhrDwGstYdCiP2f+ymCoJlijMFqN4muNhCEF5NqMdodlNFBrF2KCCkj\nmkZ7pw3AWmQk1hoPwjDqu8ZTt+s+J8+XYDS7u9sI5TeOSJLnS058I3NZVoxGucMp+/vx6PF9bl6/\nxa/+yi8D8P57Dzg7uyDPC+ZeaOr6zaeYTC7Y39/n7MwtCIvVCpAsG7doFVVFlqSYPGc6c++7eUti\nDMHlZlk4okJVVUGhEFxdsxXOAqjbyaLaayadxoOKsdI3UJQzT2ibLEI4RTvdrCd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YrtrxHVcs4zQhnEz6QZjHVeGqMhPGN8ICZkTpVGaMMHdAxcrBPMkUOZe1QEGyl9dJa5v1FCo4TG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\niDYZc2Az8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c4sz/GlL34BGUQ0bf1/Z3+f+flZ4mTo/E/jtEuSJC6zXL19h3JU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\n+OhHP8rXvvY1AL71rW/x3ve+l7U17aG9v7tLpRJysL9rJ/JarcrKsSXW7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EpFmqdkKrc9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4tl9/9KucyyJfy059g2JZeH038/37RMkiRkPFYKZNSRGY/3ZmRJjLQsbRxtQNcd93dTYXnS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\n2qQQGJ8Cq5tcms+RN2mLB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jis9mg4jEsaJTOOpzNnK5Y2ONCjk2O3lCp9akUolEhiqmNr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\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AVRywznFtpGwdW599pcgpyF1/SzH3wpnqhLCUkS9hCUtYwkMO96XIiegKgH8M4AIcH/1r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\nLGEJDzksEfkSlrCEJTzk8P8D1XqsvIoYpfYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9848c771d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 6))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.imshow(cv2.cvtColor(img_test, cv2.COLOR_BGR2RGB))\n",
    "from matplotlib import patches\n",
    "for f in found:\n",
    "    ax.add_patch(patches.Rectangle((f[0], f[1]), f[2], f[3], color='y', linewidth=3, fill=False))\n",
    "plt.savefig('detected.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Implementing Our First Support Vector Machine](06.01-Implementing-Your-First-Support-Vector-Machine.ipynb) | [Contents](../README.md) | [Detecting Pedestrians with Support Vector Machines](06.03-Additional-SVM-Exercises.ipynb) >"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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